Data-driven Sensitivity Analysis of Controllability Measure for Network Systems

Optimization of controllability, i.e., to optimize the degree of the controllability using the flexibility of a system such as choosing input nodes and designing network structure, is growing in importance to network systems. For example, if a network system has strong controllability for a certain...

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Bibliographic Details
Published inProceedings of the IEEE Conference on Decision & Control pp. 4098 - 4103
Main Authors Banno, Ikumi, Azuma, Shun-ichi, Ariizumi, Ryo, Asai, Toru, Imura, Jun-ichi
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.12.2022
Subjects
Controllability
Faces
Mathematical models
Network systems
Optimization
Sensitivity analysis
Weight measurement
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Summary:Optimization of controllability, i.e., to optimize the degree of the controllability using the flexibility of a system such as choosing input nodes and designing network structure, is growing in importance to network systems. For example, if a network system has strong controllability for a certain control input, we know that the input is effective for controlling the system even under a limited energy condition. If a mathematical model of the system is available, the controllability can be optimized by a model-based method. However, we often face the difficulty to incorporate useful prior knowledge of the system. In such a case, a data-driven approach is promising. In this paper, as the initial step toward this direction, we address the problem of determining the sensitivity of a controllability measure with respect to an edge weight of a network system in a data-driven manner. By characterizing the sensitivity by two Lyapunov equations, we clarify that the sensitivity is derived from the so-called data-driven Lyapunov equations. Moreover, this result is extended to the case of high-order sensitivity.
ISSN:2576-2370
DOI:10.1109/CDC51059.2022.9992345